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Record W4319437397 · doi:10.5539/gjhs.v15n2p42

Comparison of 3D Printing and Traditional Hand Orthosis Fabrication

2023· article· en· W4319437397 on OpenAlexvenueaboutno aff
John Damiao, Natalie Calianese, Daniel Cartwright, Cynthia Mary Cherian, Erica Lee, Danielle Mucek

Bibliographic record

VenueGlobal Journal of Health Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
Fundersnot available
KeywordsOrthoticsSplint (medicine)SplintsFabricationWristLikert scaleAssistive technologyMedicinePhysical therapy3D printing3d printedPhysical medicine and rehabilitationComputer scienceEngineeringBiomedical engineeringOrthodonticsPsychologyMechanical engineeringHuman–computer interactionSurgery

Abstract

fetched live from OpenAlex

Traditional methods of custom orthosis fabrication are prone to challenges and limitations. Three-dimensional (3D) printing has been piloted with lower extremity orthotics and worthy of exploration with upper extremities. The aim of this study was to compare three-dimensionally printed wrist immobilization splints to conventionally made orthoses in terms of fabrication, comfort, and functionality. Three healthy participants with no history of wrist or hand conditions were recruited to be fitted for conventional and 3D-printed wrist immobilization splints. A sequential mixed-methods study design was conducted to explore comfort, fabrication, and functionality. An ethnographic study was conducted afterward to further understand the fabrication process of 3D-printed orthotics. The Quebec User Evaluation of Satisfaction with Assistive Technology and a Splint Analysis form was used to assess comfort. The function was assessed using the Jebsen-Taylor Hand Function Test. A five-point satisfaction Likert scale was used to evaluate fabrication. Although the results were not statistically significant due to the small sample size, 3D-printed orthotics appear to provide some benefits over traditional methods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.113

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.350
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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